learning_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step) #神经网络反向传播算法,使用梯度下降算法GradientDescentOptimizer来优化权重值,learning_rate为学习率,minimize中参数loss是损失函数,global_step表明了当前迭代次数(会被自动更新) 一般来说,初始学习率、衰减系数...
fromtorch.optim.lr_schedulerimportStepLR# torch.optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=- 1, verbose=False)deftrain_step():passbegin_epoch=0max_epoch=200# 从 0 到 200 epoch# 绘制 200 轮的学习率曲线defshow_lr(begin_epoch,max_epoch,scheduler):lr=[]...
from torch.optim.lr_scheduler import PolynomialLRscheduler = PolynomialLR(optimizer, total_iters = 8, # The number of steps that the scheduler decays the learning rate. power = 1) # The power of the polynomial.下图为power= 1时的学习率衰减结果。power= 1时，学习率衰减如下所示。7、Cos...
def state_dict(self):"""Returns the state of the scheduler as a :class:`dict`.It contains an entryforevery variableinself.__dict__whichis not the optimizer."""return{key: valueforkey, valueinself.__dict__.items()ifkey!='optimizer'}def load_state_dict(self, state_dict):"""Loads ...
参考:https://pytorch.org/docs/master/optim.html#how-to-adjust-learning-rate torch.optim.lr_scheduler提供了几种方法来根据迭代的数量来调整学习率 自己手动定义一个学习率衰减函数: def adjust_learning_rate(optimizer, epoch, lr):"""Sets the learning rate to the initial LR decayed by 10 every 2 ...
torch.optim.lr_sheduler.ExponentialLR(optimizer, gamma, last_epoch) 参数: gamma(float):学习率调整倍数的底数,指数为epoch,初始值我lr, 倍数为 其它参数同上。 指数衰减调整学习率:gamma=0.9 (4) 余弦退火函数调整学习率: 学习率呈余弦函数型衰减,并以2*T_max为余弦函数周期,epoch=0对应余弦型学习率调整曲...
) for images, target in tqdm(train_dataloader): images, target = images.to(device), target.to(device) images.requires_grad=True optimizer.zero_grad() #Applying gradient checkpointing segments = 2 # get the modules in the model. These modules should be in the order ...
pytorch中这段代码的意思是把学习率learning_rate设为0.000001 但是设置学习率不是给learning_rate赋值就可以完成的,在pytorch中设置learning_rate有六种方法(这里的LR就是LearningRate的缩写)1等步长间隔调整学习率 optim.lr_scheduler.StepLR(optimizer, step_size, gamma=0.1, last_epoch=-1)2cosine...
optimizer: 神经网络训练中使用的优化器,如optimizer=torch.optim.SGD(...) step_size(int): 学习率下降间隔数,单位是epoch,而不是iteration. gamma(float): 学习率调整倍数,默认为0.1 last_epoch(int): 上一个epoch数,这个变量用来指示学习率是否需要调整。当last_epoch符合设定的间隔时,就会对学习率进行调整;...
from torch.optim.lr_scheduler import CyclicLR scheduler = CyclicLR(optimizer, base_lr = 0.0001, # Initial learning rate which is the lower boundary in the cycle for each parameter group max_lr = 1e-3, # Upper learning rate boundaries in the cycle for each parameter group ...